Customer Success Lead
Leads a segment programme or a set of strategic accounts and coaches other Customer Success Managers.
1task in this job
13skills it uses
5tools named
The skills this job leans on most
- 1Building cohort and retention analyses with correct denominatorsNeeded in 1 of 1 tasks
- 2Designing and weighting rule-based scoring modelsNeeded in 1 of 1 tasks
- 3Analysing customer lifetime value and unit economicsCore to this role
ToolsGainsightPlanhatTotangoMicrosoft ExcelOracle NetSuiteMicrosoft PowerPointMicrosoft Power BI
All 1 tasks, where people get stuck and all 13 skills
Tasks
Where people get stuck
- Building cohort and retention analyses with correct denominators
- Designing and weighting rule-based scoring models
All skills
- Building cohort and retention analyses with correct denominators
- Analysing staged funnels: conversion, time in stage and slippage
- Constructing and challenging customer segments
- Designing and validating digital analytics event measurement
- Diagnosing digital funnel conversion from event data
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Mapping customer journeys and evidence gaps
- Measuring campaign outcomes with valid denominators
- Reading business metrics
- Reconstructing a record's state at a point in time from history and audit trails
- Sampling and reviewing a team's work against standards
- Writing data quality rules
Paths to start
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Skill Path
Customer, Revenue and Sales Analytics
Analyse pipeline, forecast quality, segments and lifetime value from CRM and billing data.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonProduct and Digital Behaviour Analytics
2tasks in this job
39skills it uses
5tools named
The skills this job leans on most
- 1Building cohort and retention analyses with correct denominatorsNeeded in 1 of 2 tasks
- 2Designing and weighting rule-based scoring modelsNeeded in 1 of 2 tasks
- 3Analysing customer lifetime value and unit economicsCore to this role
ToolsGainsightPlanhatTotangoMicrosoft ExcelOracle NetSuite
All 2 tasks, where people get stuck and all 39 skills
Tasks
- Assess customer account health and churn risk, calibrate the health score and prioritise intervention
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
Where people get stuck
- Building cohort and retention analyses with correct denominators
- Designing and weighting rule-based scoring models
All skills
- Building cohort and retention analyses with correct denominators
- Designing and weighting rule-based scoring models
- Analysing staged funnels: conversion, time in stage and slippage
- Assessing data-driven fairness and bias
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Challenging probability-weighted sales forecasts
- Classifying AI uses against regulatory obligations
- Constructing and challenging customer segments
- Designing and validating digital analytics event measurement
- Designing experiments
- Diagnosing digital funnel conversion from event data
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Escalating unresolved risks with evidence and clear ownership
- Estimating and challenging causal effects
- Evaluating ML generalisation and fitness
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Giving specific developmental feedback on data and AI work
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Mapping customer journeys and evidence gaps
- Measuring campaign outcomes with valid denominators
- Monitoring model fitness and drift
- Preparing leakage-resistant ML data
- Reading business metrics
- Recognising and resolving ethical conflicts in data and AI work
- Reconstructing a record's state at a point in time from history and audit trails
- Sampling and reviewing a team's work against standards
- Setting and reviewing action thresholds for scores and model predictions
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
Paths to start
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Skill Path
Customer, Revenue and Sales Analytics
Analyse pipeline, forecast quality, segments and lifetime value from CRM and billing data.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonProduct and Digital Behaviour AnalyticsMachine Learning: from Data to Validated Models and Decisions
3tasks in this job
84skills it uses
7tools named
The skills this job leans on most
- 1Building cohort and retention analyses with correct denominatorsNeeded in 1 of 3 tasks
- 2Designing and weighting rule-based scoring modelsNeeded in 1 of 3 tasks
- 3Analysing customer lifetime value and unit economicsCore to this role
ToolsGainsightPlanhatTotangoMicrosoft ExcelOracle NetSuiteMicrosoft PowerPoint+ 1 more
All 3 tasks, where people get stuck and all 84 skills
Tasks
- Assess customer account health and churn risk, calibrate the health score and prioritise intervention
- Investigate customer-experience evidence
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
Where people get stuck
- Building cohort and retention analyses with correct denominators
- Designing and weighting rule-based scoring models
All skills
- Building cohort and retention analyses with correct denominators
- Designing and weighting rule-based scoring models
- Analysing event sequences, states and durations
- Analysing staged funnels: conversion, time in stage and slippage
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Building decision-focused reports and dashboards
- Calculating and interpreting percentages, rates and ratios
- Challenging probability-weighted sales forecasts
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Classifying AI uses against regulatory obligations
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Constructing and challenging customer segments
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Defining the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing and appraising samples
- Designing and interpreting hypothesis tests
- Designing and quality-checking survey instruments
- Designing and validating digital analytics event measurement
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Diagnosing digital funnel conversion from event data
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating ML generalisation and fitness
- Evaluating and improving chart accessibility
- Evaluating employee experience evidence responsibly
- Evaluating experimental results and integrity
- Evaluating responsible AI use risks
- Evaluating whether people understood, adopted and sustained a change
- Exercising human oversight of automated and AI outputs
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Fitting, checking and interpreting inferential regression models
- Formulating answerable data questions
- Giving specific developmental feedback on data and AI work
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether uncertainty changes the decision
- Mapping customer journeys and evidence gaps
- Measuring campaign outcomes with valid denominators
- Monitoring model fitness and drift
- Preparing analysis-ready datasets reproducibly
- Preparing leakage-resistant ML data
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Reconstructing a record's state at a point in time from history and audit trails
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Setting and reviewing action thresholds for scores and model predictions
- Standardising and cleaning data under agreed rules
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Training reproducible machine learning models
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
Skill Path
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Exploratory Data Analysis and Pattern Discovery
Describe a distribution honestly and decide which unusual records need checking before anyone acts.
- Modules
- 5 open
- Practice data
- Raket Parts
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
About the role
Leads a segment programme or a set of strategic accounts and coaches other Customer Success Managers. Owns retention approaches across a group of accounts and is accountable for the quality of other CSMs' risk calls. Leads without line management and judges when a health score misclassifies a whole segment.